• Earthquakes and Structures
• /
• v.14 no.5
• /
• pp.389-398
• /
• 2018

The seismic isolation system makes a structure isolated from ground motions to protect the structure from seismic events. Seismic isolation techniques have been implemented in full-scale buildings and bridges because of their simplicity, economic effectiveness, inherent stability and reliability. As for the responses of an isolated structure due to seismic events, it is well known that the most uncertain aspects are the seismic loading itself and structural properties. Due to the randomness of earthquakes and uncertainty of structures, seismic response distributions of an isolated structure are needed when evaluating the seismic fragility assessment (or probabilistic seismic safety assessment) of an isolated structure. Seismic response time histories are useful and often essential elements in its design or evaluation stage. Thus, a large number of non-linear dynamic analyses should be performed to evaluate the seismic performance of an isolated structure. However, it is a monumental task to gather the design or evaluation information of the isolated structure from too many seismic analyses, which is impractical. In this paper, a new methodology that can evaluate the seismic fragility assessment of an isolated structure is proposed by using stochastic response database, which is a device that can estimate the seismic response distributions of an isolated structure without any seismic response analyses. The seismic fragility assessment of the isolated nuclear power plant is performed using the proposed methodology. The proposed methodology is able to evaluate the seismic performance of isolated structures effectively and reduce the computational efforts tremendously.

• Earthquakes and Structures
• /
• v.9 no.5
• /
• pp.937-956
• /
• 2015

Seismic isolation systems decouple structures from ground motions to protect them from seismic events. Seismic isolation devices have been implemented in many full-scale buildings and bridges because of their simplicity, economic effectiveness, inherent stability, and reliability. It is well known that the most uncertain aspect for obtaining the accurate responses of an isolated structure from seismic events is the seismic loading itself. It is needed to know the seismic response distributions of the isolated structure resulting from the randomness of earthquakes when probabilistic designing or probabilistic evaluating an isolated structure. Earthquake time histories are useful and often an essential element for designing or evaluating isolated structures. However, it is very challenging to gather the design and evaluation information for an isolated structure from many seismic analyses. In order to evaluate the seismic performance of an isolated structure, numerous nonlinear dynamic analyses need to be performed, but this is impractical. In this paper, the concept of the stochastic response database (SRD) is defined to obtain the seismic response distributions of an isolated structure instantaneously, thereby significantly reducing the computational efforts. An equivalent model of the isolated structure is also developed to improve the applicability and practicality of the SRD. The effectiveness of the proposed methodology is numerically verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2004.05b
• /
• pp.552-556
• /
• 2004

This paper surveys the design of the adaptive information filtering agents to retrieve the useful information within a large scale database. As the information retrieval through the Internet is generalized, it is necessary to extract the useful information satisfied the user's request condition to reduce the seeking time. For the first, this module is designed by the Rough reduct to generate the reduced minimal knowledge database considered the users natural query language in a large scale knowledge database, and also it is executed the soft computing by the fuzzy composite processing to operate the uncertain value of the reduced schema domain.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.6
• /
• pp.215-223
• /
• 2010

New information technologies make it easy to access and acquire information in various ways. However, It also enable powerful and various threat to system security. The prominent database technology challenging these threats is access control. Currently, to keep pace with the new paradigms, new extended access control methods are challenged. We study access control with uncertain context. With respect to access control, it is possible that there is a discrepancy between the syntactic phrase in security policies and that in queries, called semantic gap problem. In our semantic access control, we extract semantic implications from context tree and introduce the measure factor to calculate the degree of the discrepancy, which is used to control the exceed privileges.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.123-130
• /
• 2021

A novel approach for the detection of cheating during e-Exams is presented here using convolutional neural networks (CNN) based systems. This system will help the proctors to identify any kind of uncertain event at the time of online exams, for which most of the government's across the globe are recommending due to the Covid-19 pandemic. Most of the institutions and students across the globe are badly affected by their academic programs and it is a challenging task for universities to conduct examinations using the traditional methods. Therefore, the students are attending most of their classes using different types of third party applications that are available online. However, to conduct online exams the universities cannot rely on these service providers for a long time. Therefore, in this work, a complete setup of the software tools is provided for the students, which can be used by students at their respective laptops/personal computers with strict guidelines from the university. The proposed approach helps most of the universities in Saudi Arabia to maintain their database of different events/activities of students at the time of E-Exams. This method proved to be more accurate and CNN based detection proved to be more sensitive with an accuracy of 97% to detect any kind of uncertain activity of the students at the time of e-Exam.

• Herbal Formula Science
• /
• v.29 no.1
• /
• pp.19-31
• /
• 2021

Objectives : The root of Salvia miltiorrhiza, known as 'Dansam (DS, 丹參)', is used for and treating cardiovascular diseases based on its efficacy of promoting blood circulation and breaking through a blood stasis. In this study, we would like to see if DS could be effectively used for stroke from the perspective of network pharmacology. Methods : The analysis was conducted using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database to derive the main active compounds of DS and identify the mechanism of each compound acting on the human body. The networks between compounds, target protein and disease were expressed through Cytoscape. Protein-protein interaction (PPI) analysis was performed using STRING database. Results : Fifty two active compounds of DS were identified by screening the ingredients of DS through TCMSP. Based on the networks of these compounds with target protein and disease, it can be said that DS might be effective for preventing and treating stroke. PPI result showed that adrenergic receptor has many interactions among proteins, indicating its significance in stroke pathway. Conclusion : In this study, we derived target proteins and target diseases of DS that could be used in study of stroke. However, since it is uncertain if these targets can be controlled by DS extracts or not, we would like to confirm the results with further animal experiments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.7
• /
• pp.597-603
• /
• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.40 no.5
• /
• pp.259-265
• /
• 2003

It leads to develop the query system with artificial intelligent technologies to handle inaccurate query. To develop the query system, metadata is essential to control a uncertain data, providing information about uncertainty of the data, and the classification system of metadata are necessary. This paper shows a classification of metadata based on fuzzy theory and the implementation processing to process the fuzzy query in a relational database system.

• Journal of Korean Neurosurgical Society
• /
• v.46 no.2
• /
• pp.136-143
• /
• 2009

Objective: Remote cerebellar hemorrhage (RCH) is one of the rare complications occurring after supratentorial surgery, and its pathomechanism is poorly understood. We report 10 cases of RCH from our institution and review 154 cases from a database in order to delineate incidence, common presentation, risk factors, and outcomes of this complication. In addition, the means of prevention are discussed. Methods: We reviewed the medical records of 10 patients who experienced RCH after undergoing supratentorial surgery at our institution between 2001 and 2008. A database search in Medline revealed 154 cases of RCH in the English literature. Characteristic features were analyzed and compared. Results: There were 10 cases of RCH among 3307 supratentorial surgery cases, indicating a 0.3% incidence. All patients had characteristic imaging features of RCH, namely a streaky bleeding pattern in the superior folia of the cerebellum. Seven patients had a history of preoperative hypertension. Four cases were related to cerebral aneurysms, and other four developed after the removal of brain tumors. Cerebrospinal fluid (CSF) drainage apparatuses were installed postoperatively in all cases. Outcomes according to modified Rankin scale (mRS) were good in 7 patients, with 1 fatal case. Conclusion: RCH is a rare complication after supratentorial surgery, and the exact etiology still remains uncertain. Hypertension and perioperative loss of CSF seem positively correlated with RCH, but no single risk factor is totally responsible. Patients with RCH should be closely observed to improve their prognosis.

• Journal of the Korean Society of Systems Engineering
• /
• v.18 no.2
• /
• pp.75-93
• /
• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.